This is how the AI bubble bursts.
Microsoft has introduced a new class of computers with Nvidia chips built directly into them. The idea is that some AI inference can happen locally on the computer while more demanding tasks are sent to the cloud.
That hybrid model makes complete sense for users. It could also become a serious problem for companies like OpenAI.
When you think about how most people use artificial intelligence, much of it is basically an advanced version of Google search. We ask questions, summarize documents, organize information, generate text, and use simple AI-powered tools. Most of these tasks do not require the most advanced model in the world.
They certainly do not need to be processed inside an enormous data center every single time.
A growing amount of this work can already be done locally. I use Gemma 4 on my computer for many tasks right now. If we extrapolate even one year into the future, local models are going to become dramatically faster, cheaper, and more capable.
Soon, a huge percentage of the AI tasks we perform throughout the day will happen directly on our devices. There will be no subscription fee, no token charge, and no need to send every prompt to a remote server.
For consumers, that is great. For the major AI companies, it could be devastating.
Companies like OpenAI have bet the farm on consumer artificial intelligence. Their business depends on millions of people constantly sending requests to models running in the cloud. Every prompt consumes tokens. Every generated answer requires computing power. The companies then try to cover those costs through subscriptions, enterprise contracts, and developer fees.
But inference is becoming a commodity.
As local models improve, consumers will have less reason to pay a monthly fee for routine AI tasks. Their computer or phone will be able to answer most questions instantly and privately. The cloud will only be necessary when someone needs a much more advanced model for a specialized task.
That could eliminate half to seventy-five percent of the inference currently flowing through companies like OpenAI.
We are already seeing a version of this shift inside businesses. Not long ago, the majority of enterprise inference was being handled by frontier models. Now, that balance appears to be flipping as companies move toward open-weight models because they are cheaper, more customizable, and easier to control.
Why would a business pay premium prices for every query when an open model can handle most of its workload at a fraction of the cost?
The same logic will apply to individual users.
Once capable AI becomes a standard feature inside every laptop and phone, using a local model will feel completely normal. People will not think of it as running artificial intelligence. It will simply be part of the operating system.
Your computer will summarize documents, search your files, write emails, organize your calendar, edit photographs, generate code, and answer everyday questions without contacting the cloud. Much of that functionality will be included in the price of the device.
In other words, it will feel free.
That destroys a major part of the consumer AI business model. If ordinary inference becomes free and ubiquitous, companies cannot build trillion-dollar valuations by charging people for access to it.
Their only remaining moat will be at the frontier.
OpenAI and similar companies will need to build models so extraordinarily capable that users and businesses still have a reason to pay for them. These cannot simply be slightly better chatbots. They will need to perform complex work from beginning to end, solve problems local models cannot touch, and provide enough value to justify the enormous cost of operating them.
That pushes the entire industry toward a strange economic structure. Most AI use happens locally for free, while a small number of extremely powerful models remain in the cloud for the hardest and most valuable tasks.
The market starts to look less like a subscription software business and more like a utility. People handle routine work on their own devices and only call on the cloud when they need something exceptional.
This is why the current AI boom may be based on a false assumption. Investors are valuing these companies as though the demand for cloud inference will continue expanding indefinitely. They assume that more AI usage automatically means more revenue for the companies operating the largest models.
But AI usage can grow while cloud revenue collapses.
If local models become good enough for most people, overall AI use could explode while the percentage of that activity generating revenue for frontier labs falls dramatically. The technology succeeds, but many of the companies built around it fail to capture the value.
That is how the bubble bursts.
It is not because artificial intelligence disappears. It is because artificial intelligence becomes so common, cheap, and decentralized that basic access is no longer worth paying for.
The future of AI may already be arriving. Most of it will run locally on our own devices. It will be private, fast, and essentially free.
We will only reach for the cloud when we need something truly advanced.
That is an incredible future for users. It may be a disastrous one for the companies currently betting everything on selling us tokens.
